morph-locations-final
Allison Davis Connelly
2024-02-24
Data collection
## Using libcurl 8.3.0 with Schannel## `geom_smooth()` using formula = 'y ~ x'
Location effects
Will run an ANOVA on the residuals with location and species as fixed effects. This will show me if morphology depends on the species, the location, and if the location and species interact to determine morphology. CANNOT DO WITH COUNT DATA–may have to run a glmer with zero-tuncated negative binomial/quasi-poisson distribution instead (think this will tell me if species, location, and interaction affect the trait…). Will only work with the residuals, because comparing the means of the residuals indicates differences in variation. Would I not just use Levene’s and MWU again but include spp and zone/basin/watershed?
Leaving the anovas for count data in rn until I figure out what to do.
I will first run this using the zones as the location factor. Zones (1-4) represent the latitude range with equivalent sample sizes in each, since the collections were not equally representative of all latitudes, and I wanted to avoid a sampling bias when randomly selecting samples. Zone 1 corresponds to the southern most latitude range, and zone 4 corresponds to the northern most latitude range.
I will then run the same analysis using basin as the location factor. Since fish are physically isolated to the river basins they occupy, the genetic variation is also limited to that basin. Thus it is possible for fish within the same basin to be more similar due to genetic/physical constraints. (will also do with watershed just to see).
Lastly I will run ANOVAs with both zones and basins but with standardized residuals. This would allow me to compare overall variation across traits (at least those that are depended on body size) rather than just one trait at a time. Not 100% sure if this is useful (or correct to do), but thought it would be interesting.
ANOVAs
Amazon
Zones
library(ggplot2)
lat3 <- raw2[raw2$SPP == "p.latipinna", ]
form3 <- raw2[raw2$SPP == "p.latipinna", ]
A.D <- aov(abs.res.D ~ QUARTILE, data=form3)
summary(A.D)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 2.306 0.7685 4.292 0.00635 **
## Residuals 130 23.277 0.1791
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.D)) +
geom_boxplot()
A.P1 <- aov(abs.res.P1 ~ QUARTILE, data=form3)
summary(A.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.159 0.05296 0.444 0.722
## Residuals 130 15.511 0.11932A.P1.R <- aov(abs.res.P1.R ~ QUARTILE, data=form3)
summary(A.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.065 0.02172 0.166 0.919
## Residuals 130 16.987 0.13067ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.P1.R)) +
geom_boxplot()
A.LLSC <- aov(abs.res.LLSC ~ QUARTILE, data=form3)
summary(A.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 1.31 0.4354 1.209 0.309
## Residuals 130 46.80 0.3600A.SBLL <- aov(abs.res.SBLL ~ QUARTILE, data=form3)
summary(A.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.436 0.1454 1.142 0.335
## Residuals 130 16.546 0.1273ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.SBLL)) +
geom_boxplot()
A.BD <- aov(abs.res.BD ~ QUARTILE, data=form3)
summary(A.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00594 0.001981 1.519 0.213
## Residuals 130 0.16955 0.001304ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.BD)) +
geom_boxplot()
A.CPD <- aov(abs.res.CPD ~ QUARTILE, data=form3)
summary(A.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.01249 0.004163 6.832 0.00026 ***
## Residuals 130 0.07922 0.000609
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.CPD)) +
geom_boxplot()
A.CPL <- aov(abs.res.CPL ~ QUARTILE, data=form3)
summary(A.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.02074 0.006912 9.448 1.08e-05 ***
## Residuals 130 0.09511 0.000732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.CPL)) +
geom_boxplot()
A.PreDL <- aov(abs.res.PreDL ~ QUARTILE, data=form3)
summary(A.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00209 0.0006977 1.606 0.191
## Residuals 130 0.05646 0.0004343ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.PreDL)) +
geom_boxplot()
A.DbL <- aov(abs.res.DbL ~ QUARTILE, data=form3)
summary(A.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.01219 0.004063 2.276 0.0828 .
## Residuals 130 0.23204 0.001785
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.DbL)) +
geom_boxplot()
A.HL <- aov(abs.res.HL ~ QUARTILE, data=form3)
summary(A.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00344 0.0011466 1.324 0.269
## Residuals 130 0.11260 0.0008661ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.HL)) +
geom_boxplot()
A.HD <- aov(abs.res.HD ~ QUARTILE, data=form3)
summary(A.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00672 0.002239 2.04 0.111
## Residuals 130 0.14269 0.001098A.HW <- aov(abs.res.HW ~ QUARTILE, data=form3)
summary(A.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.01136 0.003788 7.212 0.000163 ***
## Residuals 130 0.06827 0.000525
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.HW)) +
geom_boxplot()
A.SnL <- aov(abs.res.SnL ~ QUARTILE, data=form3)
summary(A.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00766 0.0025545 5.052 0.00242 **
## Residuals 130 0.06573 0.0005056
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A.OL <- aov(abs.res.OL ~ QUARTILE, data=form3)
summary(A.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 2.17e-32 7.242e-33 1.538 0.208
## Residuals 130 6.12e-31 4.708e-33ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.OL)) +
geom_boxplot()
Basins
A1.D <- aov(abs.res.D ~ BASIN, data=form3)
summary(A1.D)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 2.789 0.5579 3.133 0.0106 *
## Residuals 128 22.793 0.1781
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(BASIN), y=abs.res.D)) +
geom_boxplot()
A1.P1 <- aov(abs.res.P1 ~ BASIN, data=form3)
summary(A1.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.945 0.1891 1.643 0.153
## Residuals 128 14.725 0.1150A1.P1.R <- aov(abs.res.P1.R ~ BASIN, data=form3)
summary(A1.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.159 0.03187 0.241 0.943
## Residuals 128 16.893 0.13197ggplot(form3, aes(x=factor(BASIN), y=abs.res.P1.R)) +
geom_boxplot()
A1.LLSC <- aov(abs.res.LLSC ~ BASIN, data=form3)
summary(A1.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 4.54 0.9080 2.668 0.025 *
## Residuals 128 43.57 0.3404
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(BASIN), y=abs.res.LLSC)) +
geom_boxplot()
A1.SBLL <- aov(abs.res.SBLL ~ BASIN, data=form3)
summary(A1.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.755 0.1510 1.191 0.317
## Residuals 128 16.228 0.1268ggplot(form3, aes(x=factor(BASIN), y=abs.res.SBLL)) +
geom_boxplot()
A1.BD <- aov(abs.res.BD ~ BASIN, data=form3)
summary(A1.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.00786 0.001571 1.2 0.313
## Residuals 128 0.16764 0.001310ggplot(form3, aes(x=factor(BASIN), y=abs.res.BD)) +
geom_boxplot()
A1.CPD <- aov(abs.res.CPD ~ BASIN, data=form3)
summary(A1.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.01713 0.003425 5.879 6.34e-05 ***
## Residuals 128 0.07458 0.000583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(BASIN), y=abs.res.CPD)) +
geom_boxplot()
A1.CPL <- aov(abs.res.CPL ~ BASIN, data=form3)
summary(A1.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.02013 0.004025 5.383 0.000159 ***
## Residuals 128 0.09572 0.000748
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A1.PreDL <- aov(abs.res.PreDL ~ BASIN, data=form3)
summary(A1.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.00492 0.0009846 2.35 0.0445 *
## Residuals 128 0.05363 0.0004190
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(BASIN), y=abs.res.PreDL)) +
geom_boxplot()
A1.DbL <- aov(abs.res.DbL ~ BASIN, data=form3)
summary(A1.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.00996 0.001992 1.088 0.37
## Residuals 128 0.23428 0.001830ggplot(form3, aes(x=factor(BASIN), y=abs.res.DbL)) +
geom_boxplot()
A1.HL <- aov(abs.res.HL ~ BASIN, data=form3)
summary(A1.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.00766 0.0015321 1.809 0.115
## Residuals 128 0.10838 0.0008467ggplot(form3, aes(x=factor(BASIN), y=abs.res.HL)) +
geom_boxplot()
A1.HD <- aov(abs.res.HD ~ BASIN, data=form3)
summary(A1.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.00684 0.001367 1.228 0.3
## Residuals 128 0.14257 0.001114ggplot(form3, aes(x=factor(BASIN), y=abs.res.HD)) +
geom_boxplot()
A1.HW <- aov(abs.res.HW ~ BASIN, data=form3)
summary(A1.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.01460 0.0029194 5.746 8.11e-05 ***
## Residuals 128 0.06504 0.0005081
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(BASIN), y=abs.res.HW)) +
geom_boxplot()
A1.SnL <- aov(abs.res.SnL ~ BASIN, data=form3)
summary(A1.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.01931 0.003861 9.138 1.87e-07 ***
## Residuals 128 0.05409 0.000423
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A1.OL <- aov(abs.res.OL ~ BASIN, data=form3)
summary(A1.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 1.240e-32 2.479e-33 0.511 0.768
## Residuals 128 6.214e-31 4.854e-33ggplot(form3, aes(x=factor(BASIN), y=abs.res.OL)) +
geom_boxplot()
Watersheds
A2.D <- aov(abs.res.D ~ WATERSHED, data=form3)
summary(A2.D)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 3.847 0.3497 1.963 0.0378 *
## Residuals 122 21.736 0.1782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.D)) +
geom_boxplot()
A2.P1 <- aov(abs.res.P1 ~ WATERSHED, data=form3)
summary(A2.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 1.302 0.1184 1.005 0.446
## Residuals 122 14.368 0.1178A2.P1.R <- aov(abs.res.P1.R ~ WATERSHED, data=form3)
summary(A2.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.34 0.03093 0.226 0.995
## Residuals 122 16.71 0.13698ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.P1.R)) +
geom_boxplot()
A2.LLSC <- aov(abs.res.LLSC ~ WATERSHED, data=form3)
summary(A2.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 8.16 0.7422 2.267 0.015 *
## Residuals 122 39.94 0.3274
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.LLSC)) +
geom_boxplot()
A2.SBLL <- aov(abs.res.SBLL ~ WATERSHED, data=form3)
summary(A2.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 5.086 0.4623 4.741 4.93e-06 ***
## Residuals 122 11.897 0.0975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.SBLL)) +
geom_boxplot()
A2.BD <- aov(abs.res.BD ~ WATERSHED, data=form3)
summary(A2.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01278 0.001162 0.871 0.571
## Residuals 122 0.16271 0.001334ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.BD)) +
geom_boxplot()
A2.CPD <- aov(abs.res.CPD ~ WATERSHED, data=form3)
summary(A2.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01833 0.0016662 2.77 0.00304 **
## Residuals 122 0.07338 0.0006015
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.CPD)) +
geom_boxplot()
A2.CPL <- aov(abs.res.CPL ~ WATERSHED, data=form3)
summary(A2.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.03237 0.0029429 4.301 2.05e-05 ***
## Residuals 122 0.08347 0.0006842
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A2.PreDL <- aov(abs.res.PreDL ~ WATERSHED, data=form3)
summary(A2.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.00568 0.0005163 1.191 0.3
## Residuals 122 0.05288 0.0004334A2.DbL <- aov(abs.res.DbL ~ WATERSHED, data=form3)
summary(A2.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.04272 0.003883 2.351 0.0115 *
## Residuals 122 0.20152 0.001652
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.DbL)) +
geom_boxplot()
A2.HL <- aov(abs.res.HL ~ WATERSHED, data=form3)
summary(A2.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01169 0.0010627 1.243 0.267
## Residuals 122 0.10435 0.0008553ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.HL)) +
geom_boxplot()
A2.HD <- aov(abs.res.HD ~ WATERSHED, data=form3)
summary(A2.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01036 0.0009418 0.826 0.614
## Residuals 122 0.13905 0.0011397ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.HD)) +
geom_boxplot()
A2.HW <- aov(abs.res.HW ~ WATERSHED, data=form3)
summary(A2.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01704 0.0015488 3.018 0.00136 **
## Residuals 122 0.06260 0.0005131
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.HW)) +
geom_boxplot()
A2.SnL <- aov(abs.res.SnL ~ WATERSHED, data=form3)
summary(A2.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.02208 0.0020073 4.772 4.46e-06 ***
## Residuals 122 0.05131 0.0004206
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A2.OL <- aov(abs.res.OL ~ WATERSHED, data=form3)
summary(A2.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 2.610e-32 2.372e-33 0.476 0.915
## Residuals 122 6.077e-31 4.981e-33ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.OL)) +
geom_boxplot()
Sailfin
Zones
library(ggplot2)
A.D <- aov(abs.res.D ~ QUARTILE, data=lat3)
summary(A.D)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 2.306 0.7685 4.292 0.00635 **
## Residuals 130 23.277 0.1791
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.D)) +
geom_boxplot()
A.P1 <- aov(abs.res.P1 ~ QUARTILE, data=lat3)
summary(A.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.159 0.05296 0.444 0.722
## Residuals 130 15.511 0.11932A.P1.R <- aov(abs.res.P1.R ~ QUARTILE, data=lat3)
summary(A.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.065 0.02172 0.166 0.919
## Residuals 130 16.987 0.13067ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.P1.R)) +
geom_boxplot()
A.LLSC <- aov(abs.res.LLSC ~ QUARTILE, data=lat3)
summary(A.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 1.31 0.4354 1.209 0.309
## Residuals 130 46.80 0.3600A.SBLL <- aov(abs.res.SBLL ~ QUARTILE, data=lat3)
summary(A.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.436 0.1454 1.142 0.335
## Residuals 130 16.546 0.1273ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.SBLL)) +
geom_boxplot()
A.BD <- aov(abs.res.BD ~ QUARTILE, data=lat3)
summary(A.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00594 0.001981 1.519 0.213
## Residuals 130 0.16955 0.001304ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.BD)) +
geom_boxplot()
A.CPD <- aov(abs.res.CPD ~ QUARTILE, data=lat3)
summary(A.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.01249 0.004163 6.832 0.00026 ***
## Residuals 130 0.07922 0.000609
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.CPD)) +
geom_boxplot()
A.CPL <- aov(abs.res.CPL ~ QUARTILE, data=lat3)
summary(A.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.02074 0.006912 9.448 1.08e-05 ***
## Residuals 130 0.09511 0.000732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.CPL)) +
geom_boxplot()
A.PreDL <- aov(abs.res.PreDL ~ QUARTILE, data=lat3)
summary(A.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00209 0.0006977 1.606 0.191
## Residuals 130 0.05646 0.0004343ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.PreDL)) +
geom_boxplot()
A.DbL <- aov(abs.res.DbL ~ QUARTILE, data=lat3)
summary(A.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.01219 0.004063 2.276 0.0828 .
## Residuals 130 0.23204 0.001785
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.DbL)) +
geom_boxplot()
A.HL <- aov(abs.res.HL ~ QUARTILE, data=lat3)
summary(A.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00344 0.0011466 1.324 0.269
## Residuals 130 0.11260 0.0008661ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.HL)) +
geom_boxplot()
A.HD <- aov(abs.res.HD ~ QUARTILE, data=lat3)
summary(A.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00672 0.002239 2.04 0.111
## Residuals 130 0.14269 0.001098A.HW <- aov(abs.res.HW ~ QUARTILE, data=lat3)
summary(A.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.01136 0.003788 7.212 0.000163 ***
## Residuals 130 0.06827 0.000525
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.HW)) +
geom_boxplot()
A.SnL <- aov(abs.res.SnL ~ QUARTILE, data=lat3)
summary(A.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00766 0.0025545 5.052 0.00242 **
## Residuals 130 0.06573 0.0005056
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A.OL <- aov(abs.res.OL ~ QUARTILE, data=lat3)
summary(A.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 2.17e-32 7.242e-33 1.538 0.208
## Residuals 130 6.12e-31 4.708e-33ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.OL)) +
geom_boxplot()
Basins
A1.D <- aov(abs.res.D ~ BASIN, data=lat3)
summary(A1.D)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 2.789 0.5579 3.133 0.0106 *
## Residuals 128 22.793 0.1781
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(BASIN), y=abs.res.D)) +
geom_boxplot()
A1.P1 <- aov(abs.res.P1 ~ BASIN, data=lat3)
summary(A1.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.945 0.1891 1.643 0.153
## Residuals 128 14.725 0.1150A1.P1.R <- aov(abs.res.P1.R ~ BASIN, data=lat3)
summary(A1.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.159 0.03187 0.241 0.943
## Residuals 128 16.893 0.13197ggplot(lat3, aes(x=factor(BASIN), y=abs.res.P1.R)) +
geom_boxplot()
A1.LLSC <- aov(abs.res.LLSC ~ BASIN, data=lat3)
summary(A1.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 4.54 0.9080 2.668 0.025 *
## Residuals 128 43.57 0.3404
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(BASIN), y=abs.res.LLSC)) +
geom_boxplot()
A1.SBLL <- aov(abs.res.SBLL ~ BASIN, data=lat3)
summary(A1.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.755 0.1510 1.191 0.317
## Residuals 128 16.228 0.1268ggplot(lat3, aes(x=factor(BASIN), y=abs.res.SBLL)) +
geom_boxplot()
A1.BD <- aov(abs.res.BD ~ BASIN, data=lat3)
summary(A1.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.00786 0.001571 1.2 0.313
## Residuals 128 0.16764 0.001310ggplot(lat3, aes(x=factor(BASIN), y=abs.res.BD)) +
geom_boxplot()
A1.CPD <- aov(abs.res.CPD ~ BASIN, data=lat3)
summary(A1.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.01713 0.003425 5.879 6.34e-05 ***
## Residuals 128 0.07458 0.000583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(BASIN), y=abs.res.CPD)) +
geom_boxplot()
A1.CPL <- aov(abs.res.CPL ~ BASIN, data=lat3)
summary(A1.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.02013 0.004025 5.383 0.000159 ***
## Residuals 128 0.09572 0.000748
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A1.PreDL <- aov(abs.res.PreDL ~ BASIN, data=lat3)
summary(A1.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.00492 0.0009846 2.35 0.0445 *
## Residuals 128 0.05363 0.0004190
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(BASIN), y=abs.res.PreDL)) +
geom_boxplot()
A1.DbL <- aov(abs.res.DbL ~ BASIN, data=lat3)
summary(A1.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.00996 0.001992 1.088 0.37
## Residuals 128 0.23428 0.001830ggplot(lat3, aes(x=factor(BASIN), y=abs.res.DbL)) +
geom_boxplot()
A1.HL <- aov(abs.res.HL ~ BASIN, data=lat3)
summary(A1.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.00766 0.0015321 1.809 0.115
## Residuals 128 0.10838 0.0008467ggplot(lat3, aes(x=factor(BASIN), y=abs.res.HL)) +
geom_boxplot()
A1.HD <- aov(abs.res.HD ~ BASIN, data=lat3)
summary(A1.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.00684 0.001367 1.228 0.3
## Residuals 128 0.14257 0.001114ggplot(lat3, aes(x=factor(BASIN), y=abs.res.HD)) +
geom_boxplot()
A1.HW <- aov(abs.res.HW ~ BASIN, data=lat3)
summary(A1.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.01460 0.0029194 5.746 8.11e-05 ***
## Residuals 128 0.06504 0.0005081
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(BASIN), y=abs.res.HW)) +
geom_boxplot()
A1.SnL <- aov(abs.res.SnL ~ BASIN, data=lat3)
summary(A1.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.01931 0.003861 9.138 1.87e-07 ***
## Residuals 128 0.05409 0.000423
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A1.OL <- aov(abs.res.OL ~ BASIN, data=lat3)
summary(A1.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 1.240e-32 2.479e-33 0.511 0.768
## Residuals 128 6.214e-31 4.854e-33ggplot(lat3, aes(x=factor(BASIN), y=abs.res.OL)) +
geom_boxplot()
Watersheds
A2.D <- aov(abs.res.D ~ WATERSHED, data=lat3)
summary(A2.D)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 3.847 0.3497 1.963 0.0378 *
## Residuals 122 21.736 0.1782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.D)) +
geom_boxplot()
A2.P1 <- aov(abs.res.P1 ~ WATERSHED, data=lat3)
summary(A2.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 1.302 0.1184 1.005 0.446
## Residuals 122 14.368 0.1178A2.P1.R <- aov(abs.res.P1.R ~ WATERSHED, data=lat3)
summary(A2.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.34 0.03093 0.226 0.995
## Residuals 122 16.71 0.13698ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.P1.R)) +
geom_boxplot()
A2.LLSC <- aov(abs.res.LLSC ~ WATERSHED, data=lat3)
summary(A2.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 8.16 0.7422 2.267 0.015 *
## Residuals 122 39.94 0.3274
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.LLSC)) +
geom_boxplot()
A2.SBLL <- aov(abs.res.SBLL ~ WATERSHED, data=lat3)
summary(A2.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 5.086 0.4623 4.741 4.93e-06 ***
## Residuals 122 11.897 0.0975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.SBLL)) +
geom_boxplot()
A2.BD <- aov(abs.res.BD ~ WATERSHED, data=lat3)
summary(A2.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01278 0.001162 0.871 0.571
## Residuals 122 0.16271 0.001334ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.BD)) +
geom_boxplot()
A2.CPD <- aov(abs.res.CPD ~ WATERSHED, data=lat3)
summary(A2.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01833 0.0016662 2.77 0.00304 **
## Residuals 122 0.07338 0.0006015
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.CPD)) +
geom_boxplot()
A2.CPL <- aov(abs.res.CPL ~ WATERSHED, data=lat3)
summary(A2.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.03237 0.0029429 4.301 2.05e-05 ***
## Residuals 122 0.08347 0.0006842
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A2.PreDL <- aov(abs.res.PreDL ~ WATERSHED, data=lat3)
summary(A2.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.00568 0.0005163 1.191 0.3
## Residuals 122 0.05288 0.0004334A2.DbL <- aov(abs.res.DbL ~ WATERSHED, data=lat3)
summary(A2.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.04272 0.003883 2.351 0.0115 *
## Residuals 122 0.20152 0.001652
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.DbL)) +
geom_boxplot()
A2.HL <- aov(abs.res.HL ~ WATERSHED, data=lat3)
summary(A2.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01169 0.0010627 1.243 0.267
## Residuals 122 0.10435 0.0008553ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.HL)) +
geom_boxplot()
A2.HD <- aov(abs.res.HD ~ WATERSHED, data=lat3)
summary(A2.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01036 0.0009418 0.826 0.614
## Residuals 122 0.13905 0.0011397ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.HD)) +
geom_boxplot()
A2.HW <- aov(abs.res.HW ~ WATERSHED, data=lat3)
summary(A2.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01704 0.0015488 3.018 0.00136 **
## Residuals 122 0.06260 0.0005131
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.HW)) +
geom_boxplot()
A2.SnL <- aov(abs.res.SnL ~ WATERSHED, data=lat3)
summary(A2.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.02208 0.0020073 4.772 4.46e-06 ***
## Residuals 122 0.05131 0.0004206
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A2.OL <- aov(abs.res.OL ~ WATERSHED, data=lat3)
summary(A2.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 2.610e-32 2.372e-33 0.476 0.915
## Residuals 122 6.077e-31 4.981e-33ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.OL)) +
geom_boxplot()
Both
Zones
library(ggplot2)
A.D <- aov(abs.res.D ~ SPP*QUARTILE, data=raw3)
summary(A.D)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.06 0.0635 0.408 0.52344
## QUARTILE 3 0.45 0.1510 0.970 0.40715
## SPP:QUARTILE 3 3.22 1.0719 6.888 0.00017 ***
## Residuals 292 45.44 0.1556
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.D, fill=SPP)) +
geom_boxplot()
A.P1 <- aov(abs.res.P1 ~ SPP*QUARTILE, data=raw3)
summary(A.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.29 0.2878 1.554 0.214
## QUARTILE 3 1.10 0.3663 1.977 0.117
## SPP:QUARTILE 3 0.73 0.2427 1.310 0.271
## Residuals 292 54.10 0.1853A.P1.R <- aov(abs.res.P1.R ~ SPP*QUARTILE, data=raw3)
summary(A.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 1.80 1.7983 11.423 0.000824 ***
## QUARTILE 3 0.13 0.0434 0.276 0.842918
## SPP:QUARTILE 3 0.06 0.0189 0.120 0.948162
## Residuals 292 45.97 0.1574
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.P1.R, fill=SPP)) +
geom_boxplot()
A.LLSC <- aov(abs.res.LLSC ~ SPP*QUARTILE, data=raw3)
summary(A.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 1.40 1.4013 2.940 0.0875 .
## QUARTILE 3 1.35 0.4506 0.945 0.4190
## SPP:QUARTILE 3 3.42 1.1399 2.391 0.0688 .
## Residuals 292 139.19 0.4767
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A.SBLL <- aov(abs.res.SBLL ~ SPP*QUARTILE, data=raw3)
summary(A.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.65 0.6523 4.859 0.0283 *
## QUARTILE 3 0.78 0.2591 1.930 0.1248
## SPP:QUARTILE 3 0.12 0.0390 0.291 0.8320
## Residuals 292 39.20 0.1342
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.SBLL, fill=SPP)) +
geom_boxplot()
A.BD <- aov(abs.res.BD ~ SPP*QUARTILE, data=raw3)
summary(A.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.0000 0.0000346 0.026 0.8726
## QUARTILE 3 0.0015 0.0004855 0.361 0.7811
## SPP:QUARTILE 3 0.0087 0.0028846 2.146 0.0946 .
## Residuals 292 0.3925 0.0013442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.BD, fill=SPP)) +
geom_boxplot()
A.CPD <- aov(abs.res.CPD ~ SPP*QUARTILE, data=raw3)
summary(A.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00312 0.0031247 4.900 0.02764 *
## QUARTILE 3 0.00786 0.0026201 4.108 0.00707 **
## SPP:QUARTILE 3 0.00713 0.0023775 3.728 0.01176 *
## Residuals 292 0.18622 0.0006377
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.CPD, fill=SPP)) +
geom_boxplot()
A.CPL <- aov(abs.res.CPL ~ SPP*QUARTILE, data=raw3)
summary(A.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00000 0.000000 0.000 0.986836
## QUARTILE 3 0.01640 0.005468 6.588 0.000254 ***
## SPP:QUARTILE 3 0.02033 0.006777 8.166 3.08e-05 ***
## Residuals 292 0.24236 0.000830
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.CPL, fill=SPP)) +
geom_boxplot()
A.PreDL <- aov(abs.res.PreDL ~ SPP*QUARTILE, data=raw3)
summary(A.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00006 0.0000590 0.145 0.7035
## QUARTILE 3 0.00243 0.0008091 1.991 0.1155
## SPP:QUARTILE 3 0.00392 0.0013082 3.218 0.0232 *
## Residuals 292 0.11869 0.0004065
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.PreDL, fill=SPP)) +
geom_boxplot()
A.DbL <- aov(abs.res.DbL ~ SPP*QUARTILE, data=raw3)
summary(A.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.0001 0.000068 0.037 0.847
## QUARTILE 3 0.0060 0.001997 1.093 0.352
## SPP:QUARTILE 3 0.0068 0.002277 1.246 0.293
## Residuals 292 0.5335 0.001827ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.DbL, fill=SPP)) +
geom_boxplot()
A.HL <- aov(abs.res.HL ~ SPP*QUARTILE, data=raw3)
summary(A.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00081 0.000812 0.861 0.3541
## QUARTILE 3 0.02099 0.006995 7.421 8.33e-05 ***
## SPP:QUARTILE 3 0.01029 0.003432 3.640 0.0132 *
## Residuals 292 0.27527 0.000943
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.HL, fill=SPP)) +
geom_boxplot()
A.HD <- aov(abs.res.HD ~ SPP*QUARTILE, data=raw3)
summary(A.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00000 0.0000000 0.000 0.999
## QUARTILE 3 0.00443 0.0014757 1.522 0.209
## SPP:QUARTILE 3 0.00582 0.0019390 1.999 0.114
## Residuals 292 0.28319 0.0009698A.HW <- aov(abs.res.HW ~ SPP*QUARTILE, data=raw3)
summary(A.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00213 0.0021307 2.861 0.09184 .
## QUARTILE 3 0.00917 0.0030561 4.103 0.00712 **
## SPP:QUARTILE 3 0.00710 0.0023664 3.177 0.02448 *
## Residuals 292 0.21750 0.0007449
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.HW, fill=SPP)) +
geom_boxplot()
A.SnL <- aov(abs.res.SnL ~ SPP*QUARTILE, data=raw3)
summary(A.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00009 0.000095 0.163 0.6867
## QUARTILE 3 0.01743 0.005809 9.995 2.73e-06 ***
## SPP:QUARTILE 3 0.00494 0.001647 2.833 0.0386 *
## Residuals 292 0.16972 0.000581
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A.OL <- aov(abs.res.OL ~ SPP*QUARTILE, data=raw3)
summary(A.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 4.900e-33 4.915e-33 2.108 0.1476
## QUARTILE 3 1.830e-32 6.103e-33 2.617 0.0512 .
## SPP:QUARTILE 3 5.800e-33 1.948e-33 0.835 0.4754
## Residuals 292 6.809e-31 2.332e-33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.OL, fill=SPP)) +
geom_boxplot()
Basins
A1.D <- aov(abs.res.D ~ SPP*BASIN, data=raw3)
summary(A1.D)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.06 0.0635 0.402 0.52666
## BASIN 6 3.12 0.5205 3.293 0.00377 **
## SPP:BASIN 3 0.31 0.1028 0.650 0.58344
## Residuals 289 45.68 0.1581
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(BASIN), y=abs.res.D, fill=SPP)) +
geom_boxplot()
A1.P1 <- aov(abs.res.P1 ~ SPP*BASIN, data=raw3)
summary(A1.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.29 0.2878 1.528 0.217
## BASIN 6 0.97 0.1613 0.856 0.528
## SPP:BASIN 3 0.53 0.1751 0.930 0.427
## Residuals 289 54.43 0.1883A1.P1.R <- aov(abs.res.P1.R ~ SPP*BASIN, data=raw3)
summary(A1.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 1.80 1.7983 11.421 0.000826 ***
## BASIN 6 0.51 0.0845 0.537 0.780341
## SPP:BASIN 3 0.15 0.0485 0.308 0.819718
## Residuals 289 45.50 0.1575
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(BASIN), y=abs.res.P1.R, fill=SPP)) +
geom_boxplot()
A1.LLSC <- aov(abs.res.LLSC ~ SPP*BASIN, data=raw3)
summary(A1.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 1.40 1.4013 2.999 0.0844 .
## BASIN 6 6.83 1.1376 2.435 0.0260 *
## SPP:BASIN 3 2.11 0.7026 1.504 0.2137
## Residuals 289 135.02 0.4672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(BASIN), y=abs.res.LLSC, fill=SPP)) +
geom_boxplot()
A1.SBLL <- aov(abs.res.SBLL ~ SPP*BASIN, data=raw3)
summary(A1.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.65 0.6523 4.895 0.0277 *
## BASIN 6 1.07 0.1785 1.340 0.2393
## SPP:BASIN 3 0.51 0.1716 1.288 0.2788
## Residuals 289 38.51 0.1332
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(BASIN), y=abs.res.SBLL, fill=SPP)) +
geom_boxplot()
A1.BD <- aov(abs.res.BD ~ SPP*BASIN, data=raw3)
summary(A1.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.0000 0.000035 0.027 0.8701
## BASIN 6 0.0190 0.003167 2.450 0.0251 *
## SPP:BASIN 3 0.0101 0.003367 2.605 0.0520 .
## Residuals 289 0.3735 0.001292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(BASIN), y=abs.res.BD, fill=SPP)) +
geom_boxplot()
A1.CPD <- aov(abs.res.CPD ~ SPP*BASIN, data=raw3)
summary(A1.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00312 0.0031247 4.984 0.026343 *
## BASIN 6 0.01576 0.0026273 4.191 0.000464 ***
## SPP:BASIN 3 0.00428 0.0014261 2.275 0.080070 .
## Residuals 289 0.18117 0.0006269
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(BASIN), y=abs.res.CPD, fill=SPP)) +
geom_boxplot()
A1.CPL <- aov(abs.res.CPL ~ SPP*BASIN, data=raw3)
summary(A1.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00000 0.000000 0.000 0.987
## BASIN 6 0.03228 0.005380 6.320 2.92e-06 ***
## SPP:BASIN 3 0.00078 0.000259 0.304 0.822
## Residuals 289 0.24603 0.000851
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A1.PreDL <- aov(abs.res.PreDL ~ SPP*BASIN, data=raw3)
summary(A1.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00006 0.0000590 0.147 0.7020
## BASIN 6 0.00466 0.0007773 1.932 0.0755 .
## SPP:BASIN 3 0.00412 0.0013741 3.416 0.0178 *
## Residuals 289 0.11626 0.0004023
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(BASIN), y=abs.res.PreDL, fill=SPP)) +
geom_boxplot()
A1.DbL <- aov(abs.res.DbL ~ SPP*BASIN, data=raw3)
summary(A1.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.0001 0.000068 0.037 0.847
## BASIN 6 0.0152 0.002539 1.397 0.216
## SPP:BASIN 3 0.0057 0.001914 1.053 0.370
## Residuals 289 0.5254 0.001818ggplot(raw3, aes(x=factor(BASIN), y=abs.res.DbL, fill=SPP)) +
geom_boxplot()
A1.HL <- aov(abs.res.HL ~ SPP*BASIN, data=raw3)
summary(A1.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00081 0.000812 0.827 0.36379
## BASIN 6 0.02039 0.003398 3.463 0.00255 **
## SPP:BASIN 3 0.00256 0.000852 0.868 0.45782
## Residuals 289 0.28360 0.000981
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(BASIN), y=abs.res.HL, fill=SPP)) +
geom_boxplot()
A1.HD <- aov(abs.res.HD ~ SPP*BASIN, data=raw3)
summary(A1.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00000 0.0000000 0.000 0.999
## BASIN 6 0.00478 0.0007959 0.808 0.565
## SPP:BASIN 3 0.00382 0.0012743 1.293 0.277
## Residuals 289 0.28484 0.0009856ggplot(raw3, aes(x=factor(BASIN), y=abs.res.HD, fill=SPP)) +
geom_boxplot()
A1.HW <- aov(abs.res.HW ~ SPP*BASIN, data=raw3)
summary(A1.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00213 0.0021307 2.825 0.09387 .
## BASIN 6 0.01505 0.0025085 3.326 0.00349 **
## SPP:BASIN 3 0.00076 0.0002544 0.337 0.79838
## Residuals 289 0.21795 0.0007542
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(BASIN), y=abs.res.HW, fill=SPP)) +
geom_boxplot()
A1.SnL <- aov(abs.res.SnL ~ SPP*BASIN, data=raw3)
summary(A1.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00009 0.000095 0.162 0.688
## BASIN 6 0.02054 0.003424 5.849 8.99e-06 ***
## SPP:BASIN 3 0.00238 0.000794 1.357 0.256
## Residuals 289 0.16916 0.000585
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A1.OL <- aov(abs.res.OL ~ SPP*BASIN, data=raw3)
summary(A1.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 4.900e-33 4.915e-33 2.054 0.153
## BASIN 6 9.200e-33 1.535e-33 0.641 0.697
## SPP:BASIN 3 4.100e-33 1.358e-33 0.567 0.637
## Residuals 289 6.917e-31 2.394e-33ggplot(raw3, aes(x=factor(BASIN), y=abs.res.OL, fill=SPP)) +
geom_boxplot()
Watersheds
A2.D <- aov(abs.res.D ~ SPP*WATERSHED, data=raw3)
summary(A2.D)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.06 0.0635 0.415 0.5198
## WATERSHED 13 4.33 0.3331 2.178 0.0106 *
## SPP:WATERSHED 5 1.96 0.3922 2.564 0.0274 *
## Residuals 280 42.82 0.1529
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.D, fill=SPP)) +
geom_boxplot()
A2.P1 <- aov(abs.res.P1 ~ SPP*WATERSHED, data=raw3)
summary(A2.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.29 0.2878 1.568 0.2116
## WATERSHED 13 3.73 0.2868 1.562 0.0957 .
## SPP:WATERSHED 5 0.78 0.1569 0.855 0.5120
## Residuals 280 51.41 0.1836
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A2.P1.R <- aov(abs.res.P1.R ~ SPP*WATERSHED, data=raw3)
summary(A2.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 1.80 1.7983 11.145 0.000957 ***
## WATERSHED 13 0.82 0.0629 0.390 0.972416
## SPP:WATERSHED 5 0.16 0.0322 0.199 0.962521
## Residuals 280 45.18 0.1614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.P1.R, fill=SPP)) +
geom_boxplot()
A2.LLSC <- aov(abs.res.LLSC ~ SPP*WATERSHED, data=raw3)
summary(A2.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 1.40 1.4013 3.299 0.0704 .
## WATERSHED 13 20.28 1.5596 3.672 2.15e-05 ***
## SPP:WATERSHED 5 4.75 0.9502 2.237 0.0509 .
## Residuals 280 118.93 0.4248
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.LLSC, fill=SPP)) +
geom_boxplot()
A2.SBLL <- aov(abs.res.SBLL ~ SPP*WATERSHED, data=raw3)
summary(A2.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.65 0.6523 5.611 0.0185 *
## WATERSHED 13 2.98 0.2289 1.969 0.0233 *
## SPP:WATERSHED 5 4.57 0.9138 7.861 6.06e-07 ***
## Residuals 280 32.55 0.1162
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.SBLL, fill=SPP)) +
geom_boxplot()
A2.BD <- aov(abs.res.BD ~ SPP*WATERSHED, data=raw3)
summary(A2.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.0000 0.000035 0.028 0.86766
## WATERSHED 13 0.0427 0.003283 2.639 0.00171 **
## SPP:WATERSHED 5 0.0115 0.002309 1.856 0.10214
## Residuals 280 0.3484 0.001244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.BD, fill=SPP)) +
geom_boxplot()
A2.CPD <- aov(abs.res.CPD ~ SPP*WATERSHED, data=raw3)
summary(A2.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00312 0.0031247 4.943 0.0270 *
## WATERSHED 13 0.01690 0.0012999 2.056 0.0169 *
## SPP:WATERSHED 5 0.00731 0.0014624 2.313 0.0441 *
## Residuals 280 0.17700 0.0006322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.CPD, fill=SPP)) +
geom_boxplot()
A2.CPL <- aov(abs.res.CPL ~ SPP*WATERSHED, data=raw3)
summary(A2.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00000 0.000000 0.000 0.9866
## WATERSHED 13 0.04566 0.003512 4.406 8.64e-07 ***
## SPP:WATERSHED 5 0.01021 0.002043 2.563 0.0275 *
## Residuals 280 0.22322 0.000797
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A2.PreDL <- aov(abs.res.PreDL ~ SPP*WATERSHED, data=raw3)
summary(A2.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00006 0.0000590 0.146 0.7022
## WATERSHED 13 0.00996 0.0007664 1.902 0.0297 *
## SPP:WATERSHED 5 0.00229 0.0004575 1.136 0.3416
## Residuals 280 0.11279 0.0004028
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A2.DbL <- aov(abs.res.DbL ~ SPP*WATERSHED, data=raw3)
summary(A2.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.0001 0.000068 0.040 0.841516
## WATERSHED 13 0.0348 0.002680 1.579 0.090441 .
## SPP:WATERSHED 5 0.0363 0.007253 4.273 0.000923 ***
## Residuals 280 0.4752 0.001697
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.DbL, fill=SPP)) +
geom_boxplot()
A2.HL <- aov(abs.res.HL ~ SPP*WATERSHED, data=raw3)
summary(A2.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00081 0.000812 0.896 0.345
## WATERSHED 13 0.05036 0.003874 4.275 1.54e-06 ***
## SPP:WATERSHED 5 0.00247 0.000494 0.546 0.742
## Residuals 280 0.25372 0.000906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.HL, fill=SPP)) +
geom_boxplot()
A2.HD <- aov(abs.res.HD ~ SPP*WATERSHED, data=raw3)
summary(A2.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00000 0.0000000 0.000 0.999
## WATERSHED 13 0.01725 0.0013271 1.371 0.173
## SPP:WATERSHED 5 0.00519 0.0010375 1.072 0.376
## Residuals 280 0.27100 0.0009678ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.HD, fill=SPP)) +
geom_boxplot()
A2.HW <- aov(abs.res.HW ~ SPP*WATERSHED, data=raw3)
summary(A2.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00213 0.0021307 2.862 0.0918 .
## WATERSHED 13 0.02008 0.0015446 2.075 0.0157 *
## SPP:WATERSHED 5 0.00521 0.0010418 1.399 0.2246
## Residuals 280 0.20848 0.0007446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.HW, fill=SPP)) +
geom_boxplot()
A2.SnL <- aov(abs.res.SnL ~ SPP*WATERSHED, data=raw3)
summary(A2.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00009 0.0000948 0.180 0.672
## WATERSHED 13 0.04027 0.0030976 5.890 1.23e-09 ***
## SPP:WATERSHED 5 0.00457 0.0009144 1.739 0.126
## Residuals 280 0.14725 0.0005259
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A2.OL <- aov(abs.res.OL ~ SPP*WATERSHED, data=raw3)
summary(A2.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 4.900e-33 4.915e-33 2.037 0.155
## WATERSHED 13 2.900e-32 2.231e-33 0.925 0.528
## SPP:WATERSHED 5 2.000e-34 4.600e-35 0.019 1.000
## Residuals 280 6.758e-31 2.414e-33ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.OL, fill=SPP)) +
geom_boxplot()
Standardized
DON’T KNOW IF THIS IS USEFUL
The ANOVAs above focus on differences of particular traits as a factor of species and location. If we want to get an idea of variation in general as a factor of species and location, we can standardize the residuals (essentially unitless z-scores of residuals).
sd.res.D <- append(abs(sd.lat.D), abs(sd.form.D))
sd.res.P1 <- append(abs(sd.lat.P1), abs(sd.form.P1))
sd.res.P1.R <- append(abs(sd.lat.P1.R), abs(sd.form.P1.R))
sd.res.LLSC<- append(abs(sd.lat.LLSC), abs(sd.form.LLSC))
sd.res.SBLL<- append(abs(sd.lat.SBLL), abs(sd.form.SBLL))
sd.res.BD<- append(abs(sd.lat.BD), abs(sd.form.BD))
sd.res.CPD<- append(abs(sd.lat.CPD), abs(sd.form.CPD))
sd.res.CPL<- append(abs(sd.lat.CPL), abs(sd.form.CPL))
sd.res.PreDL <- append(abs(sd.lat.PreDL), abs(sd.form.PreDL))
sd.res.DbL <- append(abs(sd.lat.DbL), abs(sd.form.DbL))
sd.res.HL<- append(abs(sd.lat.HL), abs(sd.form.HL))
sd.res.HD<- append(abs(sd.lat.HD), abs(sd.form.HD))
sd.res.HW <- append(abs(sd.lat.HW), abs(sd.form.HW))
sd.res.SnL <- append(abs(sd.lat.SnL), abs(sd.form.SnL))
sd.res.OL <- append(abs(sd.lat.OL), abs(sd.form.OL))
raw4 <- cbind(raw3, sd.res.D, sd.res.P1, sd.res.P1.R, sd.res.LLSC, sd.res.SBLL, sd.res.BD, sd.res.CPD, sd.res.CPL, sd.res.PreDL, sd.res.DbL, sd.res.HL, sd.res.HD, sd.res.HW, sd.res.SnL, sd.res.OL)
raw5 <- cbind(raw4[1:14], stack(raw4[53:68])) ## Warning in data.frame(..., check.names = FALSE): row names were found from a
## short variable and have been discardedlat.raw5 <- raw5[raw5$SPP == "p.latipinna",]
form.raw5 <- raw5[raw5$SPP == "p.formosa",]
######ZONES#####
A3.lat <- aov(values~QUARTILE, data=lat.raw5)
summary(A3.lat)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.6 0.2106 1.13 0.336
## Residuals 2140 398.9 0.1864A3.form <- aov(values~QUARTILE, data=form.raw5)
summary(A3.form)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 1.9 0.6213 2.851 0.0361 *
## Residuals 2652 578.0 0.2180
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#between species
A3 <- aov(values~QUARTILE*SPP, data=raw5)
summary(A3)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.8 0.2626 1.288 0.277
## SPP 1 0.1 0.0604 0.296 0.586
## QUARTILE:SPP 3 1.7 0.5578 2.736 0.042 *
## Residuals 4792 976.9 0.2039
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw5, aes(x=factor(QUARTILE), y=values, fill=SPP)) +
geom_boxplot()
######BASINS#####
A4.lat <- aov(values~BASIN, data=lat.raw5)
summary(A4.lat)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.8 0.1597 0.856 0.51
## Residuals 2138 398.7 0.1865A4.form <- aov(values~BASIN, data=form.raw5)
summary(A4.form)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 4 0.6 0.1525 0.698 0.593
## Residuals 2651 579.3 0.2185#between species
A4 <- aov(values~BASIN*SPP, data=raw5)
summary(A4)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 6 0.6 0.09760 0.478 0.825
## SPP 1 0.0 0.03245 0.159 0.690
## BASIN:SPP 3 0.8 0.27201 1.332 0.262
## Residuals 4789 978.0 0.20422ggplot(raw5, aes(x=factor(BASIN), y=values, fill=SPP)) +
geom_boxplot()
#####WATERSHEDS#####
A5.lat <- aov(values~WATERSHED, data=lat.raw5)
summary(A5.lat)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 2.7 0.2441 1.312 0.211
## Residuals 2132 396.8 0.1861A5.form <- aov(values~WATERSHED, data=form.raw5)
summary(A5.form)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 7 5.9 0.8383 3.867 0.000339 ***
## Residuals 2648 574.0 0.2168
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#between species
A5 <- aov(values~WATERSHED*SPP, data=raw5)
summary(A5)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 13 5.9 0.4574 2.252 0.00609 **
## SPP 1 0.2 0.1715 0.844 0.35818
## WATERSHED:SPP 5 2.5 0.4924 2.424 0.03332 *
## Residuals 4780 970.8 0.2031
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw5, aes(x=factor(WATERSHED), y=values, fill=SPP)) +
geom_boxplot()
GLMMs
Will run mixed models with zones/basins/watersheds as random effect. This will account for any variation seen at these levels and show us if there are any consistent species differences. Can only do this with residuals.
Zones
library(lme4)## Loading required package: Matrixlibrary(lmerTest)##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## stepMM.D <- glmer(abs.res.D ~ SPP + (1|QUARTILE), data=raw3)#originally had family=poisson, since this is count data, but because this was the residuals of count data, the values were non-integers and poisson only deals with integers. ## Warning in glmer(abs.res.D ~ SPP + (1 | QUARTILE), data = raw3): calling
## glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directly## boundary (singular) fit: see help('isSingular')summary(MM.D)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.D ~ SPP + (1 | QUARTILE)
## Data: raw3
##
## REML criterion at convergence: 318.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3483 -0.9655 -0.2478 0.7809 3.9563
##
## Random effects:
## Groups Name Variance Std.Dev.
## QUARTILE (Intercept) 0.0000 0.000
## Residual 0.1648 0.406
## Number of obs: 300, groups: QUARTILE, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.56682 0.03151 17.989
## SPPp.latipinna -0.02927 0.04715 -0.621
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.668
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')MM.P1 <- glmer(abs.res.P1 ~ SPP+ (1|QUARTILE), data=raw3)## Warning in glmer(abs.res.P1 ~ SPP + (1 | QUARTILE), data = raw3): calling
## glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM.P1)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.P1 ~ SPP + (1 | QUARTILE)
## Data: raw3
##
## REML criterion at convergence: 355.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2125 -0.7823 -0.2409 0.6284 6.7042
##
## Random effects:
## Groups Name Variance Std.Dev.
## QUARTILE (Intercept) 0.002772 0.05265
## Residual 0.185601 0.43081
## Number of obs: 300, groups: QUARTILE, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.46859 0.04477 10.468
## SPPp.latipinna 0.07076 0.05059 1.399
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.539MM.P1.R <- glmer(abs.res.P1.R ~ SPP+ (1|QUARTILE), data=raw3)## Warning in glmer(abs.res.P1.R ~ SPP + (1 | QUARTILE), data = raw3): calling
## glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directly## boundary (singular) fit: see help('isSingular')summary(MM.P1.R)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.P1.R ~ SPP + (1 | QUARTILE)
## Data: raw3
##
## REML criterion at convergence: 299.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4497 -0.7453 -0.4259 0.4658 4.2022
##
## Random effects:
## Groups Name Variance Std.Dev.
## QUARTILE (Intercept) 0.0000 0.0000
## Residual 0.1549 0.3936
## Number of obs: 300, groups: QUARTILE, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.42420 0.03055 13.887
## SPPp.latipinna 0.15574 0.04571 3.407
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.668
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')MM.LLSC <- glmer(abs.res.LLSC ~ SPP+ (1|QUARTILE), data=raw3)## Warning in glmer(abs.res.LLSC ~ SPP + (1 | QUARTILE), data = raw3): calling
## glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directly## boundary (singular) fit: see help('isSingular')summary(MM.LLSC)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.LLSC ~ SPP + (1 | QUARTILE)
## Data: raw3
##
## REML criterion at convergence: 638.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2981 -0.6895 -0.3493 0.6058 5.1291
##
## Random effects:
## Groups Name Variance Std.Dev.
## QUARTILE (Intercept) 0.0000 0.000
## Residual 0.4831 0.695
## Number of obs: 300, groups: QUARTILE, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.90388 0.05395 16.755
## SPPp.latipinna -0.13748 0.08072 -1.703
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.668
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')MM.SBLL <- glmer(abs.res.SBLL ~ SPP+ (1|QUARTILE), data=raw3)## Warning in glmer(abs.res.SBLL ~ SPP + (1 | QUARTILE), data = raw3): calling
## glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM.SBLL)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.SBLL ~ SPP + (1 | QUARTILE)
## Data: raw3
##
## REML criterion at convergence: 256.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.9763 -0.5477 -0.4390 -0.1701 4.4673
##
## Random effects:
## Groups Name Variance Std.Dev.
## QUARTILE (Intercept) 0.001931 0.04395
## Residual 0.133213 0.36498
## Number of obs: 300, groups: QUARTILE, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.32895 0.03771 8.723
## SPPp.latipinna -0.08923 0.04285 -2.082
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.542MM.BD <- glmer(abs.res.BD ~ SPP+ (1|QUARTILE), data=raw3)## Warning in glmer(abs.res.BD ~ SPP + (1 | QUARTILE), data = raw3): calling
## glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directly## boundary (singular) fit: see help('isSingular')summary(MM.BD)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.BD ~ SPP + (1 | QUARTILE)
## Data: raw3
##
## REML criterion at convergence: -1113.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2937 -0.7874 -0.2157 0.5092 3.9398
##
## Random effects:
## Groups Name Variance Std.Dev.
## QUARTILE (Intercept) 0.000000 0.00000
## Residual 0.001351 0.03676
## Number of obs: 300, groups: QUARTILE, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.0471000 0.0028529 16.51
## SPPp.latipinna 0.0006832 0.0042686 0.16
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.668
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')MM.CPD <- glmer(abs.res.CPD ~ SPP+ (1|QUARTILE), data=raw3)## Warning in glmer(abs.res.CPD ~ SPP + (1 | QUARTILE), data = raw3): calling
## glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM.CPD)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.CPD ~ SPP + (1 | QUARTILE)
## Data: raw3
##
## REML criterion at convergence: -1324.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6019 -0.6976 -0.2772 0.5266 3.6832
##
## Random effects:
## Groups Name Variance Std.Dev.
## QUARTILE (Intercept) 3.077e-05 0.005547
## Residual 6.554e-04 0.025600
## Number of obs: 300, groups: QUARTILE, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.033357 0.003554 9.386
## SPPp.latipinna 0.004898 0.003022 1.621
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.417MM.CPL <- glmer(abs.res.CPL ~ SPP+ (1|QUARTILE), data=raw3)## Warning in glmer(abs.res.CPL ~ SPP + (1 | QUARTILE), data = raw3): calling
## glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM.CPL)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.CPL ~ SPP + (1 | QUARTILE)
## Data: raw3
##
## REML criterion at convergence: -1232.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5869 -0.7228 -0.2561 0.4544 4.9638
##
## Random effects:
## Groups Name Variance Std.Dev.
## QUARTILE (Intercept) 0.0000675 0.008216
## Residual 0.0008903 0.029838
## Number of obs: 300, groups: QUARTILE, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.042114 0.004869 8.650
## SPPp.latipinna -0.002587 0.003527 -0.734
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.358MM.PreDL <- glmer(abs.res.PreDL ~ SPP+ (1|QUARTILE), data=raw3)## Warning in glmer(abs.res.PreDL ~ SPP + (1 | QUARTILE), data = raw3): calling
## glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM.PreDL)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.PreDL ~ SPP + (1 | QUARTILE)
## Data: raw3
##
## REML criterion at convergence: -1462.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3986 -0.7883 -0.1772 0.5298 3.8393
##
## Random effects:
## Groups Name Variance Std.Dev.
## QUARTILE (Intercept) 6.372e-06 0.002524
## Residual 4.160e-04 0.020396
## Number of obs: 300, groups: QUARTILE, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.026586 0.002130 12.482
## SPPp.latipinna 0.000617 0.002395 0.258
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.537MM.DbL <- glmer(abs.res.DbL ~ SPP+ (1|QUARTILE), data=raw3)## Warning in glmer(abs.res.DbL ~ SPP + (1 | QUARTILE), data = raw3): calling
## glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM.DbL)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.DbL ~ SPP + (1 | QUARTILE)
## Data: raw3
##
## REML criterion at convergence: -1022.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2054 -0.7237 -0.1983 0.5176 5.7638
##
## Random effects:
## Groups Name Variance Std.Dev.
## QUARTILE (Intercept) 3.147e-06 0.001774
## Residual 1.831e-03 0.042794
## Number of obs: 300, groups: QUARTILE, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.0510345 0.0035002 14.580
## SPPp.latipinna 0.0007607 0.0049822 0.153
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.646MM.HL <- glmer(abs.res.HL ~ SPP+ (1|QUARTILE), data=raw3)## Warning in glmer(abs.res.HL ~ SPP + (1 | QUARTILE), data = raw3): calling
## glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM.HL)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.HL ~ SPP + (1 | QUARTILE)
## Data: raw3
##
## REML criterion at convergence: -1206
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7939 -0.7208 -0.1760 0.4510 4.9490
##
## Random effects:
## Groups Name Variance Std.Dev.
## QUARTILE (Intercept) 0.0001226 0.01107
## Residual 0.0009683 0.03112
## Number of obs: 300, groups: QUARTILE, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.041696 0.006179 6.748
## SPPp.latipinna -0.005130 0.003683 -1.393
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.296MM.HD <- glmer(abs.res.HD ~ SPP+ (1|QUARTILE), data=raw3)## Warning in glmer(abs.res.HD ~ SPP + (1 | QUARTILE), data = raw3): calling
## glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM.HD)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.HD ~ SPP + (1 | QUARTILE)
## Data: raw3
##
## REML criterion at convergence: -1207.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.1443 -0.7319 -0.2454 0.4451 7.3258
##
## Random effects:
## Groups Name Variance Std.Dev.
## QUARTILE (Intercept) 6.794e-06 0.002606
## Residual 9.806e-04 0.031314
## Number of obs: 300, groups: QUARTILE, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.0352739 0.0028771 12.260
## SPPp.latipinna -0.0001526 0.0036632 -0.042
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.594MM.HW <- glmer(abs.res.HW ~ SPP+ (1|QUARTILE), data=raw3)## Warning in glmer(abs.res.HW ~ SPP + (1 | QUARTILE), data = raw3): calling
## glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM.HW)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.HW ~ SPP + (1 | QUARTILE)
## Data: raw3
##
## REML criterion at convergence: -1279.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4651 -0.7690 -0.2162 0.5507 3.8682
##
## Random effects:
## Groups Name Variance Std.Dev.
## QUARTILE (Intercept) 4.521e-05 0.006724
## Residual 7.618e-04 0.027601
## Number of obs: 300, groups: QUARTILE, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.037901 0.004135 9.166
## SPPp.latipinna -0.005911 0.003260 -1.813
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.388MM.SnL <- glmer(abs.res.SnL ~ SPP+ (1|QUARTILE), data=raw3)## Warning in glmer(abs.res.SnL ~ SPP + (1 | QUARTILE), data = raw3): calling
## glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM.SnL)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.SnL ~ SPP + (1 | QUARTILE)
## Data: raw3
##
## REML criterion at convergence: -1351.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7461 -0.7239 -0.2006 0.5339 5.7707
##
## Random effects:
## Groups Name Variance Std.Dev.
## QUARTILE (Intercept) 0.0001039 0.01019
## Residual 0.0005922 0.02433
## Number of obs: 300, groups: QUARTILE, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.035273 0.005533 6.375
## SPPp.latipinna -0.002665 0.002882 -0.925
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.260MM.OL <- glmer(abs.res.OL ~ SPP+ (1|QUARTILE), data=raw3)## Warning in glmer(abs.res.OL ~ SPP + (1 | QUARTILE), data = raw3): calling
## glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM.OL)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.OL ~ SPP + (1 | QUARTILE)
## Data: raw3
##
## REML criterion at convergence: -21533.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.4760 -0.1561 -0.0480 -0.0041 16.1580
##
## Random effects:
## Groups Name Variance Std.Dev.
## QUARTILE (Intercept) 5.722e-35 7.564e-18
## Residual 2.327e-33 4.824e-17
## Number of obs: 300, groups: QUARTILE, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.867e-18 5.591e-18 1.228
## SPPp.latipinna 7.835e-18 5.678e-18 1.380
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.491Watershed nested in Basin
MM1.D <- glmer(abs.res.D ~ SPP + (1|BASIN/WATERSHED), data=raw3)## Warning in glmer(abs.res.D ~ SPP + (1 | BASIN/WATERSHED), data = raw3): calling
## glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM1.D)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.D ~ SPP + (1 | BASIN/WATERSHED)
## Data: raw3
##
## REML criterion at convergence: 311.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6107 -0.7821 -0.1826 0.7087 3.7632
##
## Random effects:
## Groups Name Variance Std.Dev.
## WATERSHED:BASIN (Intercept) 0.0003511 0.01874
## BASIN (Intercept) 0.0110904 0.10531
## Residual 0.1573563 0.39668
## Number of obs: 300, groups: WATERSHED:BASIN, 14; BASIN, 7
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.53497 0.05752 9.30
## SPPp.latipinna -0.01618 0.04757 -0.34
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.429MM1.P1 <- glmer(abs.res.P1 ~ SPP+ (1|BASIN/WATERSHED), data=raw3)## Warning in glmer(abs.res.P1 ~ SPP + (1 | BASIN/WATERSHED), data = raw3):
## calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directly## boundary (singular) fit: see help('isSingular')summary(MM1.P1)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.P1 ~ SPP + (1 | BASIN/WATERSHED)
## Data: raw3
##
## REML criterion at convergence: 353.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3207 -0.7471 -0.2578 0.6288 6.7142
##
## Random effects:
## Groups Name Variance Std.Dev.
## WATERSHED:BASIN (Intercept) 0.007268 0.08525
## BASIN (Intercept) 0.000000 0.00000
## Residual 0.181635 0.42619
## Number of obs: 300, groups: WATERSHED:BASIN, 14; BASIN, 7
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.47671 0.04511 10.568
## SPPp.latipinna 0.06213 0.05321 1.168
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.558
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')MM1.P1.R <- glmer(abs.res.P1.R ~ SPP+ (1|BASIN/WATERSHED), data=raw3)## Warning in glmer(abs.res.P1.R ~ SPP + (1 | BASIN/WATERSHED), data = raw3):
## calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directly## boundary (singular) fit: see help('isSingular')summary(MM1.P1.R)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.P1.R ~ SPP + (1 | BASIN/WATERSHED)
## Data: raw3
##
## REML criterion at convergence: 299.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4497 -0.7453 -0.4259 0.4658 4.2022
##
## Random effects:
## Groups Name Variance Std.Dev.
## WATERSHED:BASIN (Intercept) 0.0000 0.0000
## BASIN (Intercept) 0.0000 0.0000
## Residual 0.1549 0.3936
## Number of obs: 300, groups: WATERSHED:BASIN, 14; BASIN, 7
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.42420 0.03055 13.887
## SPPp.latipinna 0.15574 0.04571 3.407
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.668
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')MM1.LLSC <- glmer(abs.res.LLSC ~ SPP+ (1|BASIN/WATERSHED), data=raw3)## Warning in glmer(abs.res.LLSC ~ SPP + (1 | BASIN/WATERSHED), data = raw3):
## calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directly## boundary (singular) fit: see help('isSingular')summary(MM1.LLSC)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.LLSC ~ SPP + (1 | BASIN/WATERSHED)
## Data: raw3
##
## REML criterion at convergence: 620
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0522 -0.6986 -0.2459 0.5793 4.4872
##
## Random effects:
## Groups Name Variance Std.Dev.
## WATERSHED:BASIN (Intercept) 6.596e-02 2.568e-01
## BASIN (Intercept) 1.668e-09 4.084e-05
## Residual 4.313e-01 6.567e-01
## Number of obs: 300, groups: WATERSHED:BASIN, 14; BASIN, 7
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.94523 0.09693 9.752
## SPPp.latipinna -0.12349 0.08732 -1.414
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.452
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')MM1.SBLL <- glmer(abs.res.SBLL ~ SPP+ (1|BASIN/WATERSHED), data=raw3)## Warning in glmer(abs.res.SBLL ~ SPP + (1 | BASIN/WATERSHED), data = raw3):
## calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directly## boundary (singular) fit: see help('isSingular')summary(MM1.SBLL)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.SBLL ~ SPP + (1 | BASIN/WATERSHED)
## Data: raw3
##
## REML criterion at convergence: 254.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.1262 -0.5674 -0.3973 -0.1771 4.2669
##
## Random effects:
## Groups Name Variance Std.Dev.
## WATERSHED:BASIN (Intercept) 0.005858 0.07654
## BASIN (Intercept) 0.000000 0.00000
## Residual 0.130078 0.36066
## Number of obs: 300, groups: WATERSHED:BASIN, 14; BASIN, 7
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.34254 0.03908 8.764
## SPPp.latipinna -0.07826 0.04527 -1.729
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.551
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')MM1.BD <- glmer(abs.res.BD ~ SPP+ (1|BASIN/WATERSHED), data=raw3)## Warning in glmer(abs.res.BD ~ SPP + (1 | BASIN/WATERSHED), data = raw3):
## calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM1.BD)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.BD ~ SPP + (1 | BASIN/WATERSHED)
## Data: raw3
##
## REML criterion at convergence: -1123.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6874 -0.7741 -0.1457 0.5355 3.8809
##
## Random effects:
## Groups Name Variance Std.Dev.
## WATERSHED:BASIN (Intercept) 1.000e-04 0.010002
## BASIN (Intercept) 2.419e-05 0.004918
## Residual 1.256e-03 0.035442
## Number of obs: 300, groups: WATERSHED:BASIN, 14; BASIN, 7
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.045107 0.004946 9.119
## SPPp.latipinna 0.003903 0.004595 0.849
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.467MM1.CPD <- glmer(abs.res.CPD ~ SPP+ (1|BASIN/WATERSHED), data=raw3)## Warning in glmer(abs.res.CPD ~ SPP + (1 | BASIN/WATERSHED), data = raw3):
## calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directly## boundary (singular) fit: see help('isSingular')summary(MM1.CPD)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.CPD ~ SPP + (1 | BASIN/WATERSHED)
## Data: raw3
##
## REML criterion at convergence: -1327.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7287 -0.7186 -0.2745 0.5424 3.5807
##
## Random effects:
## Groups Name Variance Std.Dev.
## WATERSHED:BASIN (Intercept) 0.000e+00 0.000000
## BASIN (Intercept) 8.916e-05 0.009443
## Residual 6.388e-04 0.025274
## Number of obs: 300, groups: WATERSHED:BASIN, 14; BASIN, 7
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.033462 0.004517 7.408
## SPPp.latipinna 0.005003 0.003039 1.646
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.359
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')MM1.CPL <- glmer(abs.res.CPL ~ SPP+ (1|BASIN/WATERSHED), data=raw3)## Warning in glmer(abs.res.CPL ~ SPP + (1 | BASIN/WATERSHED), data = raw3):
## calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM1.CPL)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.CPL ~ SPP + (1 | BASIN/WATERSHED)
## Data: raw3
##
## REML criterion at convergence: -1245.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7535 -0.6801 -0.2268 0.4777 5.0999
##
## Random effects:
## Groups Name Variance Std.Dev.
## WATERSHED:BASIN (Intercept) 5.811e-05 0.007623
## BASIN (Intercept) 1.526e-04 0.012353
## Residual 8.204e-04 0.028642
## Number of obs: 300, groups: WATERSHED:BASIN, 14; BASIN, 7
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.038643 0.006151 6.282
## SPPp.latipinna -0.002021 0.003769 -0.536
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.326MM1.PreDL <- glmer(abs.res.PreDL ~ SPP+ (1|BASIN/WATERSHED), data=raw3)## Warning in glmer(abs.res.PreDL ~ SPP + (1 | BASIN/WATERSHED), data = raw3):
## calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM1.PreDL)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.PreDL ~ SPP + (1 | BASIN/WATERSHED)
## Data: raw3
##
## REML criterion at convergence: -1463.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4140 -0.7964 -0.2149 0.5949 3.7976
##
## Random effects:
## Groups Name Variance Std.Dev.
## WATERSHED:BASIN (Intercept) 2.010e-05 0.004483
## BASIN (Intercept) 7.150e-06 0.002674
## Residual 4.043e-04 0.020108
## Number of obs: 300, groups: WATERSHED:BASIN, 14; BASIN, 7
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.025649 0.002570 9.980
## SPPp.latipinna 0.002561 0.002552 1.003
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.492MM1.DbL <- glmer(abs.res.DbL ~ SPP+ (1|BASIN/WATERSHED), data=raw3)## Warning in glmer(abs.res.DbL ~ SPP + (1 | BASIN/WATERSHED), data = raw3):
## calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directly## boundary (singular) fit: see help('isSingular')summary(MM1.DbL)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.DbL ~ SPP + (1 | BASIN/WATERSHED)
## Data: raw3
##
## REML criterion at convergence: -1025.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4234 -0.7319 -0.1995 0.5300 5.5681
##
## Random effects:
## Groups Name Variance Std.Dev.
## WATERSHED:BASIN (Intercept) 6.296e-05 0.007935
## BASIN (Intercept) 0.000e+00 0.000000
## Residual 1.779e-03 0.042181
## Number of obs: 300, groups: WATERSHED:BASIN, 14; BASIN, 7
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.052591 0.004362 12.057
## SPPp.latipinna 0.002230 0.005238 0.426
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.566
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')MM1.HL <- glmer(abs.res.HL ~ SPP+ (1|BASIN/WATERSHED), data=raw3)## Warning in glmer(abs.res.HL ~ SPP + (1 | BASIN/WATERSHED), data = raw3):
## calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directly## boundary (singular) fit: see help('isSingular')summary(MM1.HL)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.HL ~ SPP + (1 | BASIN/WATERSHED)
## Data: raw3
##
## REML criterion at convergence: -1214.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9869 -0.6862 -0.2460 0.4573 5.3177
##
## Random effects:
## Groups Name Variance Std.Dev.
## WATERSHED:BASIN (Intercept) 2.434e-04 1.560e-02
## BASIN (Intercept) 6.874e-13 8.291e-07
## Residual 8.994e-04 2.999e-02
## Number of obs: 300, groups: WATERSHED:BASIN, 14; BASIN, 7
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.0409257 0.0053166 7.698
## SPPp.latipinna -0.0008278 0.0040825 -0.203
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.396
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')MM1.HD <- glmer(abs.res.HD ~ SPP+ (1|BASIN/WATERSHED), data=raw3)## Warning in glmer(abs.res.HD ~ SPP + (1 | BASIN/WATERSHED), data = raw3):
## calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directly## boundary (singular) fit: see help('isSingular')summary(MM1.HD)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.HD ~ SPP + (1 | BASIN/WATERSHED)
## Data: raw3
##
## REML criterion at convergence: -1208.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3046 -0.6977 -0.2280 0.4486 7.3347
##
## Random effects:
## Groups Name Variance Std.Dev.
## WATERSHED:BASIN (Intercept) 2.674e-05 0.005171
## BASIN (Intercept) 0.000e+00 0.000000
## Residual 9.646e-04 0.031058
## Number of obs: 300, groups: WATERSHED:BASIN, 14; BASIN, 7
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.0359928 0.0030772 11.696
## SPPp.latipinna 0.0003754 0.0038188 0.098
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.580
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')MM1.HW <- glmer(abs.res.HW ~ SPP+ (1|BASIN/WATERSHED), data=raw3)## Warning in glmer(abs.res.HW ~ SPP + (1 | BASIN/WATERSHED), data = raw3):
## calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM1.HW)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.HW ~ SPP + (1 | BASIN/WATERSHED)
## Data: raw3
##
## REML criterion at convergence: -1278.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4833 -0.7386 -0.2504 0.6205 3.8136
##
## Random effects:
## Groups Name Variance Std.Dev.
## WATERSHED:BASIN (Intercept) 1.243e-05 0.003525
## BASIN (Intercept) 1.139e-04 0.010674
## Residual 7.483e-04 0.027354
## Number of obs: 300, groups: WATERSHED:BASIN, 14; BASIN, 7
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.042774 0.005171 8.272
## SPPp.latipinna -0.008136 0.003414 -2.383
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.351MM1.SnL <- glmer(abs.res.SnL ~ SPP+ (1|BASIN/WATERSHED), data=raw3)## Warning in glmer(abs.res.SnL ~ SPP + (1 | BASIN/WATERSHED), data = raw3):
## calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM1.SnL)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.SnL ~ SPP + (1 | BASIN/WATERSHED)
## Data: raw3
##
## REML criterion at convergence: -1366.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7820 -0.7010 -0.2279 0.5326 5.6120
##
## Random effects:
## Groups Name Variance Std.Dev.
## WATERSHED:BASIN (Intercept) 0.0001494 0.01222
## BASIN (Intercept) 0.0001156 0.01075
## Residual 0.0005318 0.02306
## Number of obs: 300, groups: WATERSHED:BASIN, 14; BASIN, 7
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.035699 0.006034 5.916
## SPPp.latipinna 0.002444 0.003182 0.768
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.284MM1.OL <- glmer(abs.res.OL ~ SPP+ (1|BASIN/WATERSHED), data=raw3)## Warning in glmer(abs.res.OL ~ SPP + (1 | BASIN/WATERSHED), data = raw3):
## calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is
## deprecated; please call lmer() directlysummary(MM1.OL)## Linear mixed model fit by REML ['lmerMod']
## Formula: abs.res.OL ~ SPP + (1 | BASIN/WATERSHED)
## Data: raw3
##
## REML criterion at convergence: -21531.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.4183 -0.1394 -0.0756 -0.0187 16.1619
##
## Random effects:
## Groups Name Variance Std.Dev.
## WATERSHED:BASIN (Intercept) 2.299e-35 4.795e-18
## BASIN (Intercept) 6.839e-36 2.615e-18
## Residual 2.342e-33 4.839e-17
## Number of obs: 300, groups: WATERSHED:BASIN, 14; BASIN, 7
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.473e-18 4.433e-18 1.460
## SPPp.latipinna 7.354e-18 5.778e-18 1.273
##
## Correlation of Fixed Effects:
## (Intr)
## SPPp.latpnn -0.603Levene’s w/ Basin?
Not sure if this is correct, but might try Levene’s test with zone/basin/watershed as an additional independent factor? This is for the traits that did NOT vary with body length.
Zones
library(car)## Loading required package: carData(LT2_P2L <- leveneTest(P2.L~SPP*QUARTILE, data=raw3)) #gives nothing since it's all the same value## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 7 NaN NaN
## 292(LT2_P2R <- leveneTest(P2.R~SPP*QUARTILE, data=raw3)) #same as above## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 7 NaN NaN
## 292(LT2_A <- leveneTest(A~SPP*QUARTILE, data=raw3)) ## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 7 1.0512 0.3954
## 292(LT2_SALL <- leveneTest(SALL~SPP*QUARTILE, data=raw3))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 7 5.5952 4.562e-06 ***
## 292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(LT2_SBLL <- leveneTest(SBLL~SPP*QUARTILE, data=raw3))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 7 1.1533 0.3299
## 292(LT2_SBDF <- leveneTest(SBDF~SPP*QUARTILE, data=raw3))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 7 2.0714 0.04655 *
## 292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(LT2_FLA <- leveneTest(FLA~SPP*QUARTILE, data=raw3))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 7 3.5457 0.001129 **
## 292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Basins
(LT2_P2L <- leveneTest(P2.L~SPP*BASIN, data=raw3)) #gives nothing since it's all the same value## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 10 NaN NaN
## 289(LT2_P2R <- leveneTest(P2.R~SPP*BASIN, data=raw3)) #same as above## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 10 NaN NaN
## 289(LT2_A <- leveneTest(A~SPP*BASIN, data=raw3)) ## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 10 2.7914 0.002593 **
## 289
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(LT2_SALL <- leveneTest(SALL~SPP*BASIN, data=raw3))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 10 5.9307 3.629e-08 ***
## 289
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(LT2_SBLL <- leveneTest(SBLL~SPP*BASIN, data=raw3))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 10 1.467 0.1511
## 289(LT2_SBDF <- leveneTest(SBDF~SPP*BASIN, data=raw3))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 10 3.3639 0.0003629 ***
## 289
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(LT2_FLA <- leveneTest(FLA~SPP*BASIN, data=raw3))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 10 1.4055 0.177
## 289Watersheds
(LT2_P2L <- leveneTest(P2.L~SPP*WATERSHED, data=raw3)) #gives nothing since it's all the same value## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 19 NaN NaN
## 280(LT2_P2R <- leveneTest(P2.R~SPP*WATERSHED, data=raw3)) #same as above## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 19 NaN NaN
## 280(LT2_A <- leveneTest(A~SPP*WATERSHED, data=raw3)) ## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 19 1.4755 0.09315 .
## 280
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(LT2_SALL <- leveneTest(SALL~SPP*WATERSHED, data=raw3))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 19 2.6048 0.0003655 ***
## 280
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(LT2_SBLL <- leveneTest(SBLL~SPP*WATERSHED, data=raw3))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 19 2.0999 0.005256 **
## 280
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(LT2_SBDF <- leveneTest(SBDF~SPP*WATERSHED, data=raw3))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 19 2.0915 0.005484 **
## 280
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(LT2_FLA <- leveneTest(FLA~SPP*WATERSHED, data=raw3))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 19 1.8711 0.01617 *
## 280
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ICCs
I’m hoping I can do this, but thought another interesting way of testing this would be to calculate and intraclass correlation coefficient–so looking at between-group variance compared to total variance, to see if variation is driven more from within or between group (if there is any at all…). I know lots of examples specify repeated measures, but I think the function below lets me specify that I only have one measurement per subject. I’m going to do this with raw data below, but will also repeat it with PCA scores in the PCA section, so that things are standardized for length. Since I’m interested in variation across populations, I think I would perform this within each sex; I am not sure how I would test this between sexes AND populations for each trait. Maybe I could then compare the ICC scores?
library(psych)
#ICC_result_twoway_explicit <- ICC(data = data\(value, id = data\)composite_id, grouping = data$population, model = “twoway”, type = “agreement”, unit = “single”, r0 = 1)
(ICC_L.D <- ICC(lat3))